Enhancing interoperability and information exchange between domain-specific software products for BIM is an important aspect in the Architecture, Engineering, Construction and Operations industry. Recent research started investigating methods from the areas of machine and deep learning for semantic enrichment of BIM models. However, training and evaluation of these machine learning algorithms requires sufficiently large and comprehensive datasets. This work presents IFCNet, a dataset of single-entity IFC files spanning a broad range of IFC classes containing both geometric and semantic information. Using only the geometric information of objects, the experiments show that three different deep learning models are able to achieve good classification performance.

翻译：在建筑、工程、建筑和运营行业中,加强BIM具体领域软件产品之间的互操作性和信息交流是建筑、工程、建筑和运营业的一个重要方面。最近的研究开始从机器领域和深入学习BIM模型的语义浓缩的方法进行调查,然而,对这些机器学习算法的培训和评价需要足够大和全面的数据集。这项工作提供了IFCNet,这是一个单实体ICF文档的数据集,涵盖广泛的IFC类别,包含几何和语义信息。仅使用物体的几何信息,实验表明三种不同的深层次学习模型能够取得良好的分类性能。